Improved multi-view privileged support vector machine

Multi-view learning (MVL) concentrates on the problem of learning from the data represented by multiple distinct feature sets. The consensus and complementarity principles play key roles in multi-view modeling. By exploiting the consensus principle or the complementarity principle among different views, various successful support vector machine (SVM)-based multi-view learning models have been proposed for performance improvement. Recently, a framework of learning using privileged information (LUPI) has been proposed to model data with complementary information. By bridging connections between the LUPI paradigm and multi-view learning, we have presented a privileged SVM-based two-view classification model, named PSVM-2V, satisfying both principles simultaneously. However, it can be further improved in these three aspects: (1) fully unleash the power of the complementary information among different views; (2) extend to multi-view case; (3) construct a more efficient optimization solver. Therefore, in this paper, we propose an improved privileged SVM-based model for multi-view learning, termed as IPSVM-MV. It directly follows the standard LUPI model to fully utilize the multi-view complementary information; also it is a general model for multi-view scenario, and an alternating direction method of multipliers (ADMM) is employed to solve the corresponding optimization problem efficiently. Further more, we theoretically analyze the performance of IPSVM-MV from the viewpoints of the consensus principle and the generalization error bound. Experimental results on 75 binary data sets demonstrate the effectiveness of the proposed method; here we mainly concentrate on two-view case to compare with state-of-the-art methods.